This paper addresses the problem of determining the 3-D pose of a curved rigid object from a single 2-D image. The surface of the object are assumed to be modeled with several patches, each of which be expressed by an implicit polynomial. Moreover, the sensed data are assumed to be the coordinates of those points that are on the image contours. Based on the idea of contour matching, the algorithm proposed computes the parameters defining the pose of the object, and achieves the segmentation of the sensed data and the recognition of the object.

This paper addresses the problem of estimating 3-D motion of a rigid object from a sequence of monocular 2-D images. The surface of object is assumed to be modeled with several patches, each of which is expressed by an implicit equation. The proposed method estimates the pose (i.e., the location and orientation) of object that corresponds to each image in the sequence: The sequence of the estimated poses gives the motion of the object. The estimation is done by solving a system of equations, each of which is typically an algebraic equation of low degree, that is derived from the expressions of the surface patches and image contours data: so the method does not require establishing the correspondence between successive two frames in the image sequence or computing optic flow. Allowing several-patch models for objects enables the proposed approach to deal with a great variety of objects. The paper includes a numerical example, where our aproach has been applied to a polyhedral object modeled with several patches.